# 启动代码：
# python3 deep_learning_object_detection.py -i images/DFDD31C25B663437AE561AD9CFED3601.png -p deploy.prototxt -m mobilenet_iter_73000.caffemodel

# 导入第三方库
import numpy as np
import argparse
import cv2

# 构建参数解析器并解析参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to input image")
ap.add_argument("-p", "--prototxt", required=True,
                help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
                help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.4,
                help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# 初始化类的标签和包围框的颜色
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
           "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# 导入模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])


# 加载输入的图片，为调整为300×300像素的归一化图片，构建一个blob输入
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)

#将bolb输入到神经网络进行检测
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()

# 绘制输出结果
for i in np.arange(0, detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > args["confidence"]:
        idx = int(detections[0, 0, i, 1])
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")

        # todo 此处idx 和confidence可作为结果输出给柳田泽
        label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
        print("[INFO] {}".format(label))

        cv2.rectangle(image, (startX, startY), (endX, endY),
                      COLORS[idx], 2)

        y = startY - 15 if startY - 15 > 15 else startY + 15

        cv2.putText(image, label, (startX, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)


# 展示检测结果
cv2.imshow("Output", image)
# cv2.imwrite("output.jpg",image)
cv2.waitKey(0)